{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0d355e83-63e2-4d0a-9e2e-b9d566a29793",
   "metadata": {},
   "source": [
    "# PANDAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "018313a2-7fd7-4e5c-952e-ce68a9b9d842",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "469165de-fa7e-4129-8b48-e9b54f4b7a06",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.arange(64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "582c474f-9405-48da-b702-7c332739c4f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = b.reshape(4,4,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "184bed6c-cbf2-405d-b021-b7501c64b5ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 4, 4)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f0044964-3180-4f2b-b045-0379e244a2d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11],\n",
       "        [12, 13, 14, 15]],\n",
       "\n",
       "       [[16, 17, 18, 19],\n",
       "        [20, 21, 22, 23],\n",
       "        [24, 25, 26, 27],\n",
       "        [28, 29, 30, 31]],\n",
       "\n",
       "       [[32, 33, 34, 35],\n",
       "        [36, 37, 38, 39],\n",
       "        [40, 41, 42, 43],\n",
       "        [44, 45, 46, 47]],\n",
       "\n",
       "       [[48, 49, 50, 51],\n",
       "        [52, 53, 54, 55],\n",
       "        [56, 57, 58, 59],\n",
       "        [60, 61, 62, 63]]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ba3e6769-b241-4320-a9cd-197220381ef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(42)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2,2,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4a39ee7c-ead1-4aa2-98f9-641100e09768",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 60,  70,  65, 100, 230, 150, 100, 300, 250, 150])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 时间4点，5点，... 22点\n",
    "times = np.array([6, 7, 8, 11, 12, 13, 18, 19, 20, 21])\n",
    "# 顾客人数\n",
    "customers = np.array([60, 70, 65, 100, 230, 150, 100, 300, 250, 150])\n",
    "customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cd5cb016-8474-49a7-9e60-56a5aa966ceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "customers = pd.Series(customers,times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d2d67e39-7995-4b98-b6d3-baf2aa4216ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6     100.0\n",
       "7     300.0\n",
       "8     250.0\n",
       "11      NaN\n",
       "12      NaN\n",
       "13      NaN\n",
       "18      NaN\n",
       "19      NaN\n",
       "20      NaN\n",
       "21      NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6f6c20ea-1100-4453-8b95-8ff79396cec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(nan)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers[11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "141bc9e3-5a3f-4207-934c-618a719dc238",
   "metadata": {},
   "outputs": [],
   "source": [
    "times = np.array(['6h', '7h',' 8h', '11h', '12h', '13h', '18h', '19h', '20h', '21h'])\n",
    "customers = np.array([60, 70, 65, 100, 230, 150, 100, 300, 250, 150])\n",
    "customers = pd.Series(customers,times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "06c1c219-b68d-40d3-afcf-05308cdf9878",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 60,  70,  65, 100, 230, 150, 100, 300, 250, 150])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c81d3016-72b2-4d5f-a44b-d1cc35bd2c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(70)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.iloc[1] #用数值索引的方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ffdf0527-7a01-4fd7-9836-f0ac763b8b4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 时段4点，5点，... 22点\n",
    "times = np.array([6, 7, 8, 11, 12, 13, 18, 19, 20, 21])\n",
    "# 顾客人数\n",
    "customers = np.array([60, 70, 65, 100, 230, 150, 100, 300, 250, 150])\n",
    "# 平均消费\n",
    "costs = np.array([6, 7, 8, 24, 23, 26, 45, 55, 45, 40])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "185a7225-ff6e-4f8b-8ae1-be53f6848239",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customers</th>\n",
       "      <th>costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>65</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>230</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>150</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>100</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>300</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>250</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>150</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    customers  costs\n",
       "6          60      6\n",
       "7          70      7\n",
       "8          65      8\n",
       "11        100     24\n",
       "12        230     23\n",
       "13        150     26\n",
       "18        100     45\n",
       "19        300     55\n",
       "20        250     45\n",
       "21        150     40"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'customers': customers, 'costs': costs}\n",
    "df = pd.DataFrame(data, index=times)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1626636d-f94a-4214-a5eb-d85478274ce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(100)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"customers\"][11]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c8aedfd-b401-4778-a4ac-d27520410e28",
   "metadata": {},
   "source": [
    "# 尝试创建一个随机的series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "02f0043a-1f37-42ab-96c3-51edf3200309",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 请用随机数生成一个40人的政治的分数表，分数在45-70中间的随机数，请用numpy，生成数生成一个数组\n",
    "### 数学： 50-120，英语： 40-76，专业课：60-140\n",
    "politics = 45+np.random.randint(25,size=40)\n",
    "math = 50+np.random.randint(70,size=40)\n",
    "english = 40+np.random.randint(36,size=40)\n",
    "major = 60+np.random.randint(80,size=40)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "218cbbcb-f1fe-4bee-9a64-610e6773633c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>politics</th>\n",
       "      <th>math</th>\n",
       "      <th>english</th>\n",
       "      <th>major</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>64</td>\n",
       "      <td>49</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46</td>\n",
       "      <td>114</td>\n",
       "      <td>46</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68</td>\n",
       "      <td>86</td>\n",
       "      <td>69</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>55</td>\n",
       "      <td>92</td>\n",
       "      <td>52</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54</td>\n",
       "      <td>83</td>\n",
       "      <td>66</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>57</td>\n",
       "      <td>55</td>\n",
       "      <td>50</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50</td>\n",
       "      <td>88</td>\n",
       "      <td>68</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>47</td>\n",
       "      <td>74</td>\n",
       "      <td>66</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>55</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>62</td>\n",
       "      <td>82</td>\n",
       "      <td>69</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>61</td>\n",
       "      <td>87</td>\n",
       "      <td>61</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>59</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>66</td>\n",
       "      <td>95</td>\n",
       "      <td>53</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>53</td>\n",
       "      <td>90</td>\n",
       "      <td>71</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>61</td>\n",
       "      <td>95</td>\n",
       "      <td>42</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>56</td>\n",
       "      <td>87</td>\n",
       "      <td>70</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>54</td>\n",
       "      <td>96</td>\n",
       "      <td>54</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>66</td>\n",
       "      <td>116</td>\n",
       "      <td>67</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>55</td>\n",
       "      <td>114</td>\n",
       "      <td>73</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>63</td>\n",
       "      <td>83</td>\n",
       "      <td>50</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>63</td>\n",
       "      <td>110</td>\n",
       "      <td>42</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>52</td>\n",
       "      <td>55</td>\n",
       "      <td>59</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>61</td>\n",
       "      <td>60</td>\n",
       "      <td>58</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>58</td>\n",
       "      <td>74</td>\n",
       "      <td>59</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>45</td>\n",
       "      <td>67</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>47</td>\n",
       "      <td>83</td>\n",
       "      <td>65</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>65</td>\n",
       "      <td>77</td>\n",
       "      <td>49</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>46</td>\n",
       "      <td>107</td>\n",
       "      <td>63</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>47</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61</td>\n",
       "      <td>50</td>\n",
       "      <td>49</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>62</td>\n",
       "      <td>98</td>\n",
       "      <td>74</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55</td>\n",
       "      <td>86</td>\n",
       "      <td>63</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>57</td>\n",
       "      <td>91</td>\n",
       "      <td>64</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>53</td>\n",
       "      <td>79</td>\n",
       "      <td>50</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>47</td>\n",
       "      <td>76</td>\n",
       "      <td>57</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>47</td>\n",
       "      <td>109</td>\n",
       "      <td>51</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>48</td>\n",
       "      <td>118</td>\n",
       "      <td>63</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>51</td>\n",
       "      <td>92</td>\n",
       "      <td>68</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>65</td>\n",
       "      <td>116</td>\n",
       "      <td>64</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    politics  math  english  major\n",
       "1         53    64       49    122\n",
       "2         54    80       75    115\n",
       "3         46   114       46    102\n",
       "4         68    86       69     96\n",
       "5         55    92       52    118\n",
       "6         54    83       66     78\n",
       "7         57    55       50    127\n",
       "8         50    88       68     83\n",
       "9         47    74       66     69\n",
       "10        55    51       51    109\n",
       "11        62    82       69    138\n",
       "12        61    87       61     92\n",
       "13        59    70       60    123\n",
       "14        66    95       53     90\n",
       "15        53    90       71    113\n",
       "16        61    95       42    111\n",
       "17        56    87       70    138\n",
       "18        54    96       54    119\n",
       "19        66   116       67    135\n",
       "20        55   114       73    131\n",
       "21        63    83       50    132\n",
       "22        63   110       42    135\n",
       "23        52    55       59     77\n",
       "24        61    60       58    100\n",
       "25        58    74       59     81\n",
       "26        45    67       60     60\n",
       "27        47    83       65     98\n",
       "28        65    77       49     66\n",
       "29        46   107       63     76\n",
       "30        69    69       47     61\n",
       "31        61    50       49    109\n",
       "32        62    98       74     84\n",
       "33        55    86       63    118\n",
       "34        57    91       64     96\n",
       "35        53    79       50     66\n",
       "36        47    76       57     79\n",
       "37        47   109       51     72\n",
       "38        48   118       63    121\n",
       "39        51    92       68     91\n",
       "40        65   116       64    120"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data={\"politics\":politics,\"math\":math,\"english\":english,\"major\":major}\n",
    "df = pd.DataFrame(data, index=xuehao)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "592ca526-fef9-4ea6-ae42-d8da03193b8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>politics</th>\n",
       "      <th>math</th>\n",
       "      <th>english</th>\n",
       "      <th>major</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>64</td>\n",
       "      <td>49</td>\n",
       "      <td>122</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>115</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46</td>\n",
       "      <td>114</td>\n",
       "      <td>46</td>\n",
       "      <td>102</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68</td>\n",
       "      <td>86</td>\n",
       "      <td>69</td>\n",
       "      <td>96</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>55</td>\n",
       "      <td>92</td>\n",
       "      <td>52</td>\n",
       "      <td>118</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54</td>\n",
       "      <td>83</td>\n",
       "      <td>66</td>\n",
       "      <td>78</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>57</td>\n",
       "      <td>55</td>\n",
       "      <td>50</td>\n",
       "      <td>127</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50</td>\n",
       "      <td>88</td>\n",
       "      <td>68</td>\n",
       "      <td>83</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>47</td>\n",
       "      <td>74</td>\n",
       "      <td>66</td>\n",
       "      <td>69</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>55</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>109</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>62</td>\n",
       "      <td>82</td>\n",
       "      <td>69</td>\n",
       "      <td>138</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>61</td>\n",
       "      <td>87</td>\n",
       "      <td>61</td>\n",
       "      <td>92</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>59</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>123</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>66</td>\n",
       "      <td>95</td>\n",
       "      <td>53</td>\n",
       "      <td>90</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>53</td>\n",
       "      <td>90</td>\n",
       "      <td>71</td>\n",
       "      <td>113</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>61</td>\n",
       "      <td>95</td>\n",
       "      <td>42</td>\n",
       "      <td>111</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>56</td>\n",
       "      <td>87</td>\n",
       "      <td>70</td>\n",
       "      <td>138</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>54</td>\n",
       "      <td>96</td>\n",
       "      <td>54</td>\n",
       "      <td>119</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>66</td>\n",
       "      <td>116</td>\n",
       "      <td>67</td>\n",
       "      <td>135</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>55</td>\n",
       "      <td>114</td>\n",
       "      <td>73</td>\n",
       "      <td>131</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>63</td>\n",
       "      <td>83</td>\n",
       "      <td>50</td>\n",
       "      <td>132</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>63</td>\n",
       "      <td>110</td>\n",
       "      <td>42</td>\n",
       "      <td>135</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>52</td>\n",
       "      <td>55</td>\n",
       "      <td>59</td>\n",
       "      <td>77</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>61</td>\n",
       "      <td>60</td>\n",
       "      <td>58</td>\n",
       "      <td>100</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>58</td>\n",
       "      <td>74</td>\n",
       "      <td>59</td>\n",
       "      <td>81</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>45</td>\n",
       "      <td>67</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>47</td>\n",
       "      <td>83</td>\n",
       "      <td>65</td>\n",
       "      <td>98</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>65</td>\n",
       "      <td>77</td>\n",
       "      <td>49</td>\n",
       "      <td>66</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>46</td>\n",
       "      <td>107</td>\n",
       "      <td>63</td>\n",
       "      <td>76</td>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>47</td>\n",
       "      <td>61</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>61</td>\n",
       "      <td>50</td>\n",
       "      <td>49</td>\n",
       "      <td>109</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>62</td>\n",
       "      <td>98</td>\n",
       "      <td>74</td>\n",
       "      <td>84</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>55</td>\n",
       "      <td>86</td>\n",
       "      <td>63</td>\n",
       "      <td>118</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>57</td>\n",
       "      <td>91</td>\n",
       "      <td>64</td>\n",
       "      <td>96</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>53</td>\n",
       "      <td>79</td>\n",
       "      <td>50</td>\n",
       "      <td>66</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>47</td>\n",
       "      <td>76</td>\n",
       "      <td>57</td>\n",
       "      <td>79</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>47</td>\n",
       "      <td>109</td>\n",
       "      <td>51</td>\n",
       "      <td>72</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>48</td>\n",
       "      <td>118</td>\n",
       "      <td>63</td>\n",
       "      <td>121</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>51</td>\n",
       "      <td>92</td>\n",
       "      <td>68</td>\n",
       "      <td>91</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>65</td>\n",
       "      <td>116</td>\n",
       "      <td>64</td>\n",
       "      <td>120</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    politics  math  english  major  total\n",
       "1         53    64       49    122    288\n",
       "2         54    80       75    115    324\n",
       "3         46   114       46    102    308\n",
       "4         68    86       69     96    319\n",
       "5         55    92       52    118    317\n",
       "6         54    83       66     78    281\n",
       "7         57    55       50    127    289\n",
       "8         50    88       68     83    289\n",
       "9         47    74       66     69    256\n",
       "10        55    51       51    109    266\n",
       "11        62    82       69    138    351\n",
       "12        61    87       61     92    301\n",
       "13        59    70       60    123    312\n",
       "14        66    95       53     90    304\n",
       "15        53    90       71    113    327\n",
       "16        61    95       42    111    309\n",
       "17        56    87       70    138    351\n",
       "18        54    96       54    119    323\n",
       "19        66   116       67    135    384\n",
       "20        55   114       73    131    373\n",
       "21        63    83       50    132    328\n",
       "22        63   110       42    135    350\n",
       "23        52    55       59     77    243\n",
       "24        61    60       58    100    279\n",
       "25        58    74       59     81    272\n",
       "26        45    67       60     60    232\n",
       "27        47    83       65     98    293\n",
       "28        65    77       49     66    257\n",
       "29        46   107       63     76    292\n",
       "30        69    69       47     61    246\n",
       "31        61    50       49    109    269\n",
       "32        62    98       74     84    318\n",
       "33        55    86       63    118    322\n",
       "34        57    91       64     96    308\n",
       "35        53    79       50     66    248\n",
       "36        47    76       57     79    259\n",
       "37        47   109       51     72    279\n",
       "38        48   118       63    121    350\n",
       "39        51    92       68     91    302\n",
       "40        65   116       64    120    365"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total'] = df.sum (axis = 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "336f4563-0cb2-4c0d-9971-9a0670764b12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('C:\\work\\machine-learning\\分类\\分类\\data\\data.csv')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a73aa25d-e775-4e65-810f-d9bdf2b9db95",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 4,
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   "execution_count": 5,
   "id": "5b0eb5e3-67ef-4c36-bbe2-6e25e5835bfa",
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    {
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       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica"
      ]
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       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
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      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width  species\n",
       "0           False        False         False        False    False\n",
       "1           False        False         False        False    False\n",
       "2           False        False         False        False    False\n",
       "3           False        False         False        False    False\n",
       "4           False        False         False        False    False\n",
       "..            ...          ...           ...          ...      ...\n",
       "145         False        False         False        False    False\n",
       "146         False        False         False        False    False\n",
       "147         False        False         False        False    False\n",
       "148         False        False         False        False    False\n",
       "149         False        False         False        False    False\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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    "df.isnull()\n"
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   "cell_type": "code",
   "execution_count": 7,
   "id": "022b340d-39a4-4b8e-af75-6220c30021d7",
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       "<p>150 rows × 5 columns</p>\n",
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       "     sepal_length  sepal_width  petal_length  petal_width  species\n",
       "0            True         True          True         True     True\n",
       "1            True         True          True         True     True\n",
       "2            True         True          True         True     True\n",
       "3            True         True          True         True     True\n",
       "4            True         True          True         True     True\n",
       "..            ...          ...           ...          ...      ...\n",
       "145          True         True          True         True     True\n",
       "146          True         True          True         True     True\n",
       "147          True         True          True         True     True\n",
       "148          True         True          True         True     True\n",
       "149          True         True          True         True     True\n",
       "\n",
       "[150 rows x 5 columns]"
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     "execution_count": 7,
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   "execution_count": 8,
   "id": "d9076203-d824-468a-9c67-c3e421bbd68a",
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   "outputs": [
    {
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       "      <td>150.000000</td>\n",
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       "      <th>min</th>\n",
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       "      <th>25%</th>\n",
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       "      <th>50%</th>\n",
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      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
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     "execution_count": 8,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d228e0e3-52e9-4160-b996-b325bcb40dc7",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "df=pd.read_csv('C:\\work\\machine-learning\\分类\\分类\\data\\data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "107c9432-4d48-4a91-8ec0-f239718a1c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2500x2000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(2,2,figsize=(25,20))\n",
    "species=[\"setosa\",\"versicolor\",\"virginica\"]\n",
    "color=[\"red\",\"green\",\"blue\"]\n",
    "marker=[\".\",\"s\",\"x\"]\n",
    "\n",
    "for i in range(3):\n",
    "    s=species[i]\n",
    "    data=df[df[\"species\"]==s]\n",
    "    axes[0,0].scatter(data[\"sepal_length\"],data[\"sepal_width\"],marker=marker[i],label=species[i],color=color[i])\n",
    "    axes[0,0].set_title(\"sepal_length and sepal_width\")\n",
    "    axes[0,0].set_xlabel(\"sepal_length\")\n",
    "    axes[0,0].set_ylabel(\"sepal_width\")\n",
    "    axes[0,0].legend()\n",
    "    \n",
    "for i in range(3):\n",
    "    s=species[i]\n",
    "    data=df[df[\"species\"]==s]\n",
    "    axes[0,1].scatter(data[\"sepal_length\"],data[\"petal_length\"],marker=marker[i],label=species[i],color=color[i])\n",
    "    axes[0,1].set_title(\"sepal_length and petal_length\")\n",
    "    axes[0,1].set_xlabel(\"sepal_length\")\n",
    "    axes[0,1].set_ylabel(\"petal_length\")\n",
    "    axes[0,1].legend()\n",
    "\n",
    "for i in range(3):\n",
    "    s=species[i]\n",
    "    data=df[df[\"species\"]==s]\n",
    "    axes[1,0].scatter(data[\"sepal_length\"],data[\"petal_width\"],marker=marker[i],label=species[i],color=color[i])\n",
    "    axes[1,0].set_title(\"sepal_length and petal_width\")\n",
    "    axes[1,0].set_xlabel(\"sepal_length\")\n",
    "    axes[1,0].set_ylabel(\"petal_width\")\n",
    "    axes[1,0].legend()\n",
    "    \n",
    "for i in range(3):\n",
    "    s=species[i]\n",
    "    data=df[df[\"species\"]==s]\n",
    "    axes[1,1].scatter(data[\"sepal_width\"],data[\"petal_length\"],marker=marker[i],label=species[i],color=color[i])\n",
    "    axes[1,1].set_title(\"sepal_width and petal_length\")\n",
    "    axes[1,1].set_xlabel(\"sepal_width\")\n",
    "    axes[1,1].set_ylabel(\"petal_length\")\n",
    "    axes[1,1].legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63851d0d-199a-4388-a657-088bef9f664f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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